Robotic and/or automated systems are increasingly being contemplated for the agricultural industry to improve productivity in planting, growing and/or harvesting crops. One particular application for which such systems may be of use is that of selective harvesting, where agricultural products are harvested from plants without entirely destroying the plants. Proposed robotic and/or automated approaches to selective harvesting, however, have generally relied on complex machine perception systems and hardware end-effectors that are customized for particular types of crops, and that are generally unsuitable for use with other types of crops. As an example, a machine perception system optimized to identify a green bell pepper is generally unsuitable for identifying other types of agricultural products, such as strawberries, as the agricultural products generally share little commonality across different types of crops. Moreover, for many crops, identification of an agricultural product for harvest may be complicated by the fact that the agricultural product itself is difficult to distinguish from its surrounding foliage. A green bell pepper, for example, is often nearly the same color as the leaves of a pepper plant, so color is generally not a distinguishing characteristic that can be relied upon to identify a green bell pepper for harvest.